Static maritime enviroment representation of electronic navigational charts in global path planning

In past years, numerous global path planning methods have been researched and applied in maritime surface navigation. Regardless of intended usage for either decision-support in manned, or autonomous vessel navigation, path planning should generate a safe and efficient route. However, prior to route generation, static maritime environment representation must be created first. Whether it is transformed in to discrete or continuous form, common approach is to use Electronic Navigational Charts (ENCs) as a basis for maritime environment representation. Nevertheless its origins, ENCs still adhere to inherited data generalisations and simplifications to be comprehensible for human navigators. This leads to limitations when considering path planning and spatial resolution at different chart scales. Furthermore, when generating the representation and path, uncertainty must be considered since the quality and accuracy of chart data varies. Although these topics have been addressed separately in their respective domains, their relations have not been researched in detail. The aim of the proposed paper is the review of electronic navigational charts, environment representation and common global path planning approaches’ relations. Forthcoming standards and technologies, such as usage of high-density charts, are presented and discussed as well.Peer Reviewe

A COLREGs-Compliant Decision Support Tool to Prevent Collisions at Sea

Groundings and collisions still represent the highest percentage of marine accidents despite the current attention on Maritime Education and Training and the improvement of sensor capability. Most of the time, a collision is caused by a human error with consequences ranging from moderate to severe, with a substantial impact on both environment and life safeguarded at sea. In this paper, a brief statistical data regarding human element as a root cause of marine incidents together with collision regulations misunderstanding is presented as a background chapter. Furthermore, the present work discusses a decision support system architecture to suggest an appropriate action when the risk of a potential collision is detected. The proposed architecture system is based on various modules integrated with proper sensor input data regarding the surrounding navigation area. As a result, the tool can support the Officers of Watch in the decision‐making process providing an early suggestion in compliance with the COLlision REGulations. The proposed system is intended to be used onboard independently from the degree of automation of the ship, and it is based on AIS, which is mandatory, making it widely applicable. The proper use of the system can considerably reduce the number of collisions, as demonstrated by the obtained results

Fachzeitschrift für Hydrographie und Geoinformation

Second International Issu

Robust Multi-sensor Data Fusion for Practical Unmanned Surface Vehicles (USVs) Navigation

The development of practical Unmanned Surface Vehicles (USVs) are attracting increasing attention driven by their assorted military and commercial application potential. However, addressing the uncertainties presented in practical navigational sensor measurements of an USV in maritime environment remain the main challenge of the development. This research aims to develop a multi-sensor data fusion system to autonomously provide an USV reliable navigational information on its own positions and headings as well as to detect dynamic target ships in the surrounding environment in a holistic fashion. A multi-sensor data fusion algorithm based on Unscented Kalman Filter (UKF) has been developed to generate more accurate estimations of USV’s navigational data considering practical environmental disturbances. A novel covariance matching adaptive estimation algorithm has been proposed to deal with the issues caused by unknown and varying sensor noise in practice to improve system robustness. Certain measures have been designed to determine the system reliability numerically, to recover USV trajectory during short term sensor signal loss, and to autonomously detect and discard permanently malfunctioned sensors, and thereby enabling potential sensor faults tolerance. The performance of the algorithms have been assessed by carrying out theoretical simulations as well as using experimental data collected from a real-world USV projected collaborated with Plymouth University. To increase the degree of autonomy of USVs in perceiving surrounding environments, target detection and prediction algorithms using an Automatic Identification System (AIS) in conjunction with a marine radar have been proposed to provide full detections of multiple dynamic targets in a wider coverage range, remedying the narrow detection range and sensor uncertainties of the AIS. The detection algorithms have been validated in simulations using practical environments with water current effects. The performance of developed multi-senor data fusion system in providing reliable navigational data and perceiving surrounding environment for USV navigation have been comprehensively demonstrated

Providing Nautical Chart Awareness to Autonomous Surface Vehicles

Autonomous surface vessels (ASVs) have many applications in both military and civilian domains including mine countermeasure, seafloor mapping, and physical oceanography. However, to act as effective tools, ASVs require high levels of autonomy. Currently, many commercially available ASVs have static mission plans with minimal awareness of their environment, which results in a labor intensive approach that does not scale to management of multiple vehicles. In this research, ASV autonomy was increased through the development of an intelligent mission planner and a real-time obstacle avoidance system utilizing Electronic Nautical Charts (ENCs), which describe known hazards in the marine environment without suffering from the challenges of real-time sensor processing. A new algorithm called Depth-Based A* was developed as the mission planner, where the nominal A* search algorithm was expanded by utilizing a novel cost function that balances driving in the channel with taking the most direct route on an ENC-derived cost map. Although charted obstacles can typically be avoided through mission planners, there is still an advantage in having the code do this. However, since it enables even higher levels of autonomy (e.g., “go in this area, but avoid all known obstacles”) they must still be accounted for in real time as other behaviors (i.e., avoiding uncharted obstacles or vessels) might cause the ASV to deviate from the planned path. The reactive obstacle avoidance system developed in this research reorganizes the ENC into a quick-search database where ENC-based obstacles in the ASV’s proximity are determined and avoided. These algorithms were tested with both a Seafloor System EchoBoat and ASV Global C-Worker 4 in simulation and in the field using an EchoBoat, where they avoided both concave and convex polygons. The algorithms developed in this research provide the ASV with a higher level of autonomy, potentially allowing for the same number of human operators to manage more ASVs

Navigators’ Behavior in Traffic Separation Schemes

One of the areas of decision support in the navigational ship conduct process is a Traffic Separation Scheme. TSSs are established in areas with high traffic density, often near the shore and in port approaches. The main purpose of these schemes is to improve maritime safety by channeling vessel traffic into streams. Traffic regulations as well as ships behavior in real conditions in chosen TSSs have been analyzed in order to develop decision support algorithms

Space programs summary no. 37-56, volume 1 for the period 1 January to 28 February 1969. Flight projects

Mariner Mars 1969 Project, Mariner Mars 1971 Project, and Viking Project description